Patents by Inventor Apoorv Khandelwal
Apoorv Khandelwal has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11429915Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.Type: GrantFiled: November 30, 2017Date of Patent: August 30, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Gio Borje, Benjamin John McCann, David DiCato, Jerry Lin, Skylar Payne, Apoorv Khandelwal, Nadeem Anjum
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Patent number: 11010688Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.Type: GrantFiled: November 30, 2017Date of Patent: May 18, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Gio Borje, Benjamin John McCann, David DiCato, Jerry Lin, Skylar Payne, Apoorv Khandelwal, Nadeem Anjum
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Patent number: 10679188Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.Type: GrantFiled: November 30, 2017Date of Patent: June 9, 2020Assignee: Microsoft Technology Licensing, LLCInventors: Gio Borje, Benjamin John McCann, David DiCato, Jerry Lin, Skylar Payne, Apoorv Khandelwal, Nadeem Anjum
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Publication number: 20200005217Abstract: The disclosed embodiments provide a system for processing data. During operation, the system determines impression discounting features for ordering a set of candidates that match parameters of a search from a recruiter, wherein the impression discounting features include a recruiter-candidate feature indicating interaction between the recruiter and a candidate and a candidate popularity feature indicating interaction between the candidate and a set of recruiters. Next, the system applies a machine learning model to the impression discounting features and features for the set of candidates to produce a first set of scores for personalizing a ranking of the set of candidates for the recruiter. The system then generates the ranking according to the first set of scores. Finally, the system outputs, to the recruiter, at least a portion of the ranking as search results of the search.Type: ApplicationFiled: June 29, 2018Publication date: January 2, 2020Applicant: Microsoft Technology Licensing, LLCInventors: Prakhar Sharma, Apoorv Khandelwal, Lei Ni, Erik Buchanan
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Patent number: 10521489Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.Type: GrantFiled: November 30, 2017Date of Patent: December 31, 2019Assignee: Microsoft Technology Licensing, LLCInventors: Gio Borje, Benjamin John McCann, David DiCato, Jerry Lin, Skylar Payne, Apoorv Khandelwal, Nadeem Anjum
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Publication number: 20190163668Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.Type: ApplicationFiled: November 30, 2017Publication date: May 30, 2019Inventors: Gio Borje, Benjamin John McCann, David DiCato, Jerry Lin, Skylar Payne, Apoorv Khandelwal, Nadeem Anjum
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Publication number: 20190163718Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.Type: ApplicationFiled: November 30, 2017Publication date: May 30, 2019Inventors: Gio Borje, Benjamin John McCann, David DiCato, Jerry Lin, Skylar Payne, Apoorv Khandelwal, Nadeem Anjum
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Publication number: 20190164096Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.Type: ApplicationFiled: November 30, 2017Publication date: May 30, 2019Inventors: Gio Borje, Benjamin John McCann, David DiCato, Jerry Lin, Skylar Payne, Apoorv Khandelwal, Nadeem Anjum
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Publication number: 20190164132Abstract: Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.Type: ApplicationFiled: November 30, 2017Publication date: May 30, 2019Inventors: Gio Borje, Benjamin John McCann, David DiCato, Jerry Lin, Skylar Payne, Apoorv Khandelwal, Nadeem Anjum